Skip to content
DatabasesAuto-generatedScore: 46

AWS Application Discovery Service MCP Server

The AWS Application Discovery Service, provided by Amazon Web Services (AWS), is a foundational tool designed to simplify and de-risk the complex process of migrating on-premises data center environments to the cloud.

Quick Start Summary

The AWS Application Discovery Service MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the AWS Application Discovery Service API through natural language. It exposes 10 API endpoints as callable tools, such as AssociateConfigurationItemsToApplication, BatchDeleteImportData, CreateApplication, and more. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/amazonaws-com-discovery. This integration is sourced from the auto AWS Application Discovery Service OpenAPI specification (v2015-11-01) and has a quality score of 46/99 (fair documentation coverage).

10Endpointstools mapped
NoneAuthopen access
46/99Qualityfair
~30 secSetupno auth

Server Details

Category
Databases
Authentication
None
Endpoints
10 operations
Transport
STDIO
Spec Version
v2015-11-01
Install Command
npx -y @mcp/amazonaws-com-discovery

Environment Variables

AWS_APPLICATION_DISCOVERY_SERVICE_API_KEY

Example: your_aws_application_discovery_service_api_key

Top Endpoints

POST
/#X-Amz-Target=AWSPoseidonService_V2015_11_01.AssociateConfigurationItemsToApplication

AssociateConfigurationItemsToApplication

POST
/#X-Amz-Target=AWSPoseidonService_V2015_11_01.BatchDeleteImportData

BatchDeleteImportData

POST
/#X-Amz-Target=AWSPoseidonService_V2015_11_01.CreateApplication

CreateApplication

POST
/#X-Amz-Target=AWSPoseidonService_V2015_11_01.CreateTags

CreateTags

POST
/#X-Amz-Target=AWSPoseidonService_V2015_11_01.DeleteApplications

DeleteApplications

Own this API?

Verify ownership of this listing to control the description, configuration details, and documentation links. Choose between free manual verification or instant premium placement.

Option 1: Free Verification

Slow manual review. Requires creating a GitHub issue with verified documentation or domain verification.

  • • Verified badge on page
  • • Standard search sorting
  • • 2-3 business days review
Start Free Claim →
Instant & Boosted

Option 2: Featured Upgrade($9/mo)

Instant verification plus premium styling, featured badges, and directory placement boost.

  • • ★ Featured star & amber highlight border
  • • Top of directory search placement
  • • Instant activation via claim token

📖 Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

Capabilities & Use Cases
The AWS Application Discovery Service, provided by Amazon Web Services (AWS), is a foundational tool designed to simplify and de-risk the complex process of migrating on-premises data center environments to the cloud. Its core capability lies in the automated discovery, collection, and analysis of critical infrastructure data, including server configurations, performance metrics, utilization patterns, and network dependencies. By deploying lightweight agents on physical servers or VMs, or by leveraging agentless discovery through existing data sources like VMware vCenter, the service constructs a detailed inventory and dependency map of the application portfolio. This data is instrumental for enterprise migration planning, enabling accurate Total Cost of Ownership (TCO) calculations for AWS migration services like AWS Migration Hub, AWS Server Migration Service, and AWS Database Migration Service. Typical use cases include data center consolidation, cloud readiness assessments, application modernization planning, and maintaining an accurate, up-to-date IT asset inventory.
🤖AI Agent Value
When this service's API is exposed as a set of tools via the Model Context Protocol (MCP) to an AI coding assistant, it transforms from a standalone discovery console into a dynamic, programmable resource for intelligent migration orchestration. An AI agent like Claude Desktop or Cline gains the ability to directly interact with the discovery data lifecycle, moving beyond static analysis to perform real-time, contextual operations. The value lies in enabling the AI to act as a migration planner and execution coordinator, automatically enriching application portfolios with discovered infrastructure context, validating migration groupings, and updating project metadata based on live or recent scan data. This integration bridges the gap between descriptive data and actionable insight, allowing the AI to not only read but also actively manage the foundational dataset of a migration project.
💬Example Workflows
A developer can instruct the AI agent to perform a variety of dynamic, integrated tasks. For example, they can command it to "Create a new application container in AWS Application Discovery Service for the 'Customer Portal' project and associate the previously discovered servers identified by their configuration IDs." The AI would execute the CreateApplication and AssociateConfigurationItemsToApplication calls sequentially. Another workflow could involve the instruction: "Generate a summary report of all exported configuration data from the last month, identify any servers with less than 20% CPU utilization, and tag those servers in the discovery service with 'LowUtilization' for later decommission analysis." This would combine DescribeExportConfigurations to find the data, process the information, and then use CreateTags to update the asset records. Furthermore, an agent could be tasked to "Clean up test data by deleting the staging application and all its associated import records," which would trigger DeleteApplications followed by BatchDeleteImportData.
🛡️Security & Auth
Secure integration with the AWS Application Discovery Service API is paramount. Although the endpoint authentication mechanism is listed as "None," this refers to the direct HTTP endpoint signature; in practice, all API calls must be authenticated and authorized using AWS Identity and Access Management (IAM). Developers must configure the AI agent's execution environment with secure AWS credentials, preferably via an IAM role with temporary credentials (e.g., AWS STS AssumeRole) rather than long-lived access keys. The principle of least privilege is critical; the IAM policy attached to the role or user should grant only the specific permissions required for the intended tasks (e.g., discovery:CreateApplication, discovery:DescribeAgents), and should be scoped to the relevant AWS account and, if possible, specific resources. All communication should be secured with TLS, and audit logs via AWS CloudTrail should be enabled to monitor all API activity for compliance and forensic analysis.

Similar APIs

Other APIs in the Databases category.

PostgreSQL (MCP)

Query and manage PostgreSQL databases directly from your AI agent. Read schemas, run queries, and manage data.

Database Credentials

Notion API

The Notion API is a comprehensive RESTful interface provided by Notion, the popular all-in-one workspace platform, enabling programmatic interaction with its rich set of collaborative objects. It grants developers and automated systems the ability to read, create, update, and manage core Notion entities such as blocks (the fundamental building blocks of content like text, lists, and media), databases (structured tables with properties), pages (containers for content and databases), and comments. Typical use cases span enterprise and consumer scenarios, including automating team workflows, syncing data between Notion and other business systems (like CRM, project management, or analytics tools), building custom dashboards, generating dynamic reports, and enhancing content collaboration through programmatic updates. Organizations leverage this API to break down data silos, enforce process automation, and create tailored integrations that extend Notion's native capabilities for specific departmental or cross-functional needs.

Amazon CloudWatch Application Insights

Amazon CloudWatch Application Insights is a specialized observability service provided by Amazon Web Services (AWS) designed to simplify the monitoring and troubleshooting of applications, particularly those built on Microsoft IIS and .NET frameworks running on EC2 instances or within Elastic Beanstalk environments. Its core capability lies in automatically discovering application components, analyzing correlated metrics, logs, and traces to identify anomalies, and then surfacing actionable insights that pinpoint the root cause of common operational issues. By integrating seamlessly with other AWS services like CloudWatch, AWS X-Ray, and AWS Systems Manager, it provides a unified view of application health, reducing the mean time to resolution (MTTR) for performance degradations and errors. The typical use case spans enterprise environments managing distributed microservices or monolithic .NET applications, where teams need to proactively detect issues such as memory leaks, high CPU utilization, or specific application errors without manually configuring complex monitoring dashboards and alarms.

Application Auto Scaling

The Application Auto Scaling API, provided by Amazon Web Services (AWS), is a robust service designed to automate the scaling of computing resources for a wide array of AWS services, ensuring optimal performance, availability, and cost efficiency. Its core capability is to define policies that automatically adjust the provisioned capacity of supported resources in response to changing demand, as measured by CloudWatch metrics or predefined schedules. Beyond the initially listed resources, it supports scaling for Amazon DynamoDB tables and global secondary indexes, Amazon ECS services running on Fargate or EC2, Amazon ElastiCache replication groups, Amazon Neptune clusters, Amazon SageMaker endpoint variants, and custom resources via the AWS Lambda-backed scalable target. This makes it a central tool for architects and DevOps engineers in building resilient, self-optimizing cloud architectures. Typical enterprise use cases include dynamically adjusting the number of Aurora read replicas to handle database query load spikes, scaling ECS task counts during peak traffic for a microservices application, or optimizing costs by scaling down SageMaker inference endpoints during off-hours.

Related MCP Server Integrations

PostgreSQL (MCP) MCP Setup

Query and manage PostgreSQL databases directly from your AI agent. Read schemas, run queries, and manage data.

DatabasesConfigure →

Notion API MCP Setup

The Notion API is a comprehensive RESTful interface provided by Notion, the popular all-in-one workspace platform, enabling programmatic interaction with its rich set of collaborative objects. It grants developers and automated systems the ability to read, create, update, and manage core Notion entities such as blocks (the fundamental building blocks of content like text, lists, and media), databases (structured tables with properties), pages (containers for content and databases), and comments. Typical use cases span enterprise and consumer scenarios, including automating team workflows, syncing data between Notion and other business systems (like CRM, project management, or analytics tools), building custom dashboards, generating dynamic reports, and enhancing content collaboration through programmatic updates. Organizations leverage this API to break down data silos, enforce process automation, and create tailored integrations that extend Notion's native capabilities for specific departmental or cross-functional needs.

DatabasesConfigure →

Amazon CloudWatch Application Insights MCP Setup

Amazon CloudWatch Application Insights is a specialized observability service provided by Amazon Web Services (AWS) designed to simplify the monitoring and troubleshooting of applications, particularly those built on Microsoft IIS and .NET frameworks running on EC2 instances or within Elastic Beanstalk environments. Its core capability lies in automatically discovering application components, analyzing correlated metrics, logs, and traces to identify anomalies, and then surfacing actionable insights that pinpoint the root cause of common operational issues. By integrating seamlessly with other AWS services like CloudWatch, AWS X-Ray, and AWS Systems Manager, it provides a unified view of application health, reducing the mean time to resolution (MTTR) for performance degradations and errors. The typical use case spans enterprise environments managing distributed microservices or monolithic .NET applications, where teams need to proactively detect issues such as memory leaks, high CPU utilization, or specific application errors without manually configuring complex monitoring dashboards and alarms.

DatabasesConfigure →

Application Auto Scaling MCP Setup

The Application Auto Scaling API, provided by Amazon Web Services (AWS), is a robust service designed to automate the scaling of computing resources for a wide array of AWS services, ensuring optimal performance, availability, and cost efficiency. Its core capability is to define policies that automatically adjust the provisioned capacity of supported resources in response to changing demand, as measured by CloudWatch metrics or predefined schedules. Beyond the initially listed resources, it supports scaling for Amazon DynamoDB tables and global secondary indexes, Amazon ECS services running on Fargate or EC2, Amazon ElastiCache replication groups, Amazon Neptune clusters, Amazon SageMaker endpoint variants, and custom resources via the AWS Lambda-backed scalable target. This makes it a central tool for architects and DevOps engineers in building resilient, self-optimizing cloud architectures. Typical enterprise use cases include dynamically adjusting the number of Aurora read replicas to handle database query load spikes, scaling ECS task counts during peak traffic for a microservices application, or optimizing costs by scaling down SageMaker inference endpoints during off-hours.

DatabasesConfigure →

AWS Cost Explorer Service MCP Setup

The AWS Cost Explorer API, provided by Amazon Web Services, serves as the programmatic backbone for the Cost Explorer service, a powerful tool designed to help organizations visualize, understand, and manage their AWS cloud spending. At its core, this API enables developers and financial operations (FinOps) teams to move beyond the web console and directly query their cost and usage data, unlocking the ability to build custom dashboards, automated reports, and sophisticated cost management applications. Its capabilities range from retrieving high-level aggregated data, such as monthly service costs or daily usage totals, to drilling down into granular, resource-level details, including the specific write operations of a DynamoDB table or the data transfer metrics of an EC2 instance. This granular access is critical for enterprises implementing showback/chargeback models, identifying optimization opportunities, and enforcing budget guardrails across complex, multi-account AWS environments.

DatabasesConfigure →